SoroushVahidi/scidocs huggingface dataset:
Implicit Quantile Networks for Distributional Reinforcement Learning
(2018)

For the merging function m, the simplest choice would be a simple vector concatenation of ψ(x) and φ(τ). Note however, that the MLP f which takes in the output of m and outputs the action-value quantiles, only has a single hidden layer in the DQN network. Therefore, to force a sufficiently early interaction between the two representations, we also considered a multiplicative function m(ψ, φ) = ψ φ, where denotes the element-wise (Hadamard) product of two vectors, as well as a ‘residual’ function m(ψ, φ) = ψ (1 + φ).
DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image
(2018)

In this paper, we propose a deep learning architecture that produces accurate dense depth for the outdoor scene from a single color image and a sparse depth. Inspired by the indoor depth completion, our network estimates surface normals as the intermediate representation to produce dense depth, and can be trained end-to-end. With a modified encoder-decoder structure, our network effectively fuses the dense color image and the sparse LiDAR depth. To address outdoor specific challenges, our network predicts a confidence mask to handle mixed LiDAR signals near foreground boundaries due to occlusion, and combines estimates from the color image and surface normals with learned attention maps to improve the depth accuracy especially for distant areas. Extensive experiments demonstrate that our model improves upon the state-of-the-art performance on KITTI depth completion benchmark. Ablation study shows the positive impact of each model components to the final performance, and comprehensive analysis shows that our model generalizes well to the input with higher sparsity or from indoor scenes.
Introduction Welcome to this early seminal work , Fostering Resiliency in Kids : Protective Factors in the Family , School and Community
(1996)

The field of prevention, both research and practice, came a long way in the1980s: from short-term, even one-shot, individual-focused interventions in the school classroom to a growing awareness and beginning implementation of long-term, comprehensive, environmental-focused interventions expanding beyond the school to include the community. Furthermore, in the mid-1980s we finally started to hear preventionists talking about prevention strategies and programs based on research identifying the underlying risk factors for problems such as alcohol and other drug abuse, teen pregnancy, delinquency and gangs, and dropping out (Hawkins, Lishner, and Catalano, 1985). While certainly a giant step in the right direction, the identification of risks does not necessarily provide us with a clear sense of just what strategies we need to implement to reduce the risks. More recently, we are hearing preventionists talk about “protective factors,” about building “resiliency” in youth, about basing our strategies on what research hast old us about the environmental factors that facilitate the development of youth who do not get involved in life-compromising problems (Benard, March 1987). What clearly becomes the challenge for the l990s is the implementation of prevention strategies that strengthen protective factors in our families, schools, an communities. As Gibbs and Bennett (1990) conceptualize the process, we must”turn the situation around by translating negative risk factors into positive action strategies” which are, in essence, protective factors. After a brief overview of the protective factor research phenomenon, this paper will discuss the major protective factors that research has identified as contributing to the development of resiliency in youth and the implications of this for building effective prevention programs.
A Novel AHRS Inertial Sensor-Based Algorithm for Wheelchair Propulsion Performance Analysis
(2016)

With the increasing rise of professionalism in sport, athletes, teams, and coaches are looking to technology to monitor performance in both games and training in order to find a competitive advantage. The use of inertial sensors has been proposed as a cost effective and adaptable measurement device for monitoring wheelchair kinematics; however, the outcomes are dependent on the reliability of the processing algorithms. Though there are a variety of algorithms that have been proposed to monitor wheelchair propulsion in court sports, they all have limitations. Through experimental testing, we have shown the Attitude and Heading Reference System (AHRS)-based algorithm to be a suitable and reliable candidate algorithm for estimating velocity, distance, and approximating trajectory. The proposed algorithm is computationally inexpensive, agnostic of wheel camber, not sensitive to sensor placement, and can be embedded for real-time implementations. The research is conducted under Griffith University Ethics (GU Ref No: 2016/294).
Tuning of PID controller based on Fruit Fly Optimization Algorithm
(2012)

The Proportional - Integral - Derivative (PID) controllers are one of the most popular controllers used in industry because of their remarkable effectiveness, simplicity of implementation and broad applicability. PID tuning is the key issue in the design of PID controllers and most of the tuning processes are implemented manually resulting in difficulty and time consuming. To enhance the capabilities of traditional PID parameters tuning techniques, modern heuristics approaches, such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are employed recent years. In this paper, a novel tuning method based on Fruit Fly Optimization Algorithm (FOA) is proposed to optimize PID controller parameters. Each fruit fly's position represents a candidate solution for PID parameters. When the fruit fly swarm flies towards one location, it is treated as the evolution of each iterative swarm. After hundreds of iteration, the tuning results - the best PID controller parameters can be obtained. The main advantages of the proposed method include ease of implementation, stable convergence characteristic, large searching range, ease of transformation of such concept into program code and ease of understanding. Simulation results demonstrate that the FOA-Based optimized PID (FOA - PID) controller is with the capability of providing satisfactory closed - loop performance.
Event detection over twitter social media streams
(2013)

In recent years, microblogs have become an important source for reporting real-world events. A real-world occurrence reported in microblogs is also called a social event. Social events may hold critical materials that describe the situations during a crisis. In real applications, such as crisis management and decision making, monitoring the critical events over social streams will enable watch officers to analyze a whole situation that is a composite event, and make the right decision based on the detailed contexts such as what is happening, where an event is happening, and who are involved. Although there has been significant research effort on detecting a target event in social networks based on a single source, in crisis, we often want to analyze the composite events contributed by different social users. So far, the problem of integrating ambiguous views from different users is not well investigated. To address this issue, we propose a novel framework to detect composite social events over streams, which fully exploits the information of social data over multiple dimensions. Specifically, we first propose a graphical model called location-time constrained topic (LTT) to capture the content, time, and location of social messages. Using LTT, a social message is represented as a probability distribution over a set of topics by inference, and the similarity between two messages is measured by the distance between their distributions. Then, the events are identified by conducting efficient similarity joins over social media streams. To accelerate the similarity join, we also propose a variable dimensional extendible hash over social streams. We have conducted extensive experiments to prove the high effectiveness and efficiency of the proposed approach.
Kullback-Leibler Divergence Constrained Distributionally Robust Optimization
(2012)

In this paper we study distributionally robust optimization (DRO) problems where the ambiguity set of the probability distribution is defined by the Kullback-Leibler (KL) divergence. We consider DRO problems where the ambiguity is in the objective function, which takes a form of an expectation, and show that the resulted minimax DRO problems can be formulated as a one-layer convex minimization problem. We also consider DRO problems where the ambiguity is in the constraint. We show that ambiguous expectation-constrained programs may be reformulated as a one-layer convex optimization problem that takes the form of the Benstein approximation of Nemirovski and Shapiro (2006). We further consider distributionally robust probabilistic programs. We show that the optimal solution of a probability minimization problem is also optimal for the distributionally robust version of the same problem, and also show that the ambiguous chance-constrained programs (CCPs) may be reformulated as the original CCP with an adjusted confidence level. A number of examples and special cases are also discussed in the paper to show that the reformulated problems may take simple forms that can be solved easily. The main contribution of the paper is to show that the KL divergence constrained DRO problems are often of the same complexity as their original stochastic programming problems and, thus, KL divergence appears a good candidate in modeling distribution ambiguities in mathematical programming.
Measuring the effectiveness of answers in Yahoo! Answers
(2015)

Purpose – This study investigates the ways in which effectiveness of answers in Yahoo! Answers, one of the largest community question answering sites (CQAs), is related to question types and answerer reputation. Effective answers are defined as those that are detailed, readable, superior in quality, and contributed promptly. Five question types that were studied include factoid, list, definition, complex interactive, and opinion. Answerer reputation refers to the past track record of answerers in the community. Design/Methodology/Approach – The dataset comprises 1,459 answers posted in Yahoo! Answers in response to 464 questions that were distributed across the five question types. The analysis was done using factorial analysis of variance. Findings – The results indicate that factoid, definition and opinion questions were comparable in attracting high quality as well as readable answers. Although reputed answerers generally fared better in offering detailed and high quality answers, novices were found to submit more readable responses. Moreover, novices were more prompt in answering factoid, list and definition questions. Originality/value – By analyzing variations in answer effectiveness with a twin-focus on question types and answerer reputation, this study explores a strand of CQA research that has hitherto received limited attention. The findings offer insights to users and designers of CQAs.
An Inference Model for Semantic Entailment in Natural Language
(2005)

Semantic entailment is the problem of determining if the meaning of a given sentence entails that of another. This is a fundamental problem in natural language understanding that provides a broad framework for studying language variability and has a large number of applications. We present a principled approach to this problem that builds on inducing rerepresentations of text snippets into a hierarchical knowledge representation along with a sound inferential mechanism that makes use of it to prove semantic entailment.
The social role of social media: the case of Chennai rains-2015
(2016)

Social media has altered the way individuals communicate in present scenario. Individuals feel more connected on Facebook and Twitter with greater communication freedom to chat, share pictures, and videos. Hence, social media is widely employed by various companies to promote their product and services and establish better customer relationships. Owing to the increasing popularity of these social media platforms, their usage is also expanding significantly. Various studies have discussed the importance of social media in the corporate world for effective marketing communication, customer relationships, and firm performance, but no studies have focused on the social role of social media, i.e., in disaster resilience in India. Various academicians and practitioners have advocated the importance and use of social media in disaster resilience. This article focuses on the role that social media can play during the time of natural disasters, with the help of the recent case of Chennai floods in India. This study provides a better understanding about the role social media can play in natural disaster resilience in Indian context.
A New Algorithm for Detecting Text Line in Handwritten Documents
(2006)

Curvilinear text line detection and segmentation in handwritten documents is a significant challenge for handwriting recognition. Given no prior knowledge of script, we model text line detection as an image segmentation problem by enhancing text line structure using a Gaussian window, and adopting the level set method to evolve text line boundaries. Experiments show that the proposed method achieves high accuracy for detecting text lines in both handwritten and machine printed documents with many scripts.
Modelling and control of a 2-DOF planar parallel manipulator for semiconductor packaging systems
(2005)

A novel direct-drive planar parallel manipulator for high-speed and high-precision semiconductor packaging systems is presented. High precision kinematics design, significant reduction on moving mass and driving power of the actuators over traditional XY motion stages are the benefits of the proposed manipulator. The mathematical model of the manipulator is obtained using the Newton-Euler method and a practical model-based control design approach is employed to design the PID computed-torque controller. Experimental results demonstrate that the proposed planar parallel manipulator has significant improvements on motion performance in terms of positioning accuracy, settling time and stability when compared with traditional XY stages. This shows that the proposed planar parallel manipulator can provide a superior alternative for replacing traditional XY motion stages in high precision low-payload applications
BuildingRules: a trigger-action based system to manage complex commercial buildings
(2015)

Modern Building Management Systems (BMSs) provide limited amount of control to its occupants, and typically allow only the facility manager to set the building policies. BuildingRules let occupants to customise their office spaces using trigger-action programming. In order to accomplish this task, BuildingRules automatically detects conflicts among the policies expressed by the users using a SMT based logic. We tested our system with 23 users across 17 days in a virtual office building, and evaluate the effectiveness and scalability of the system.
Uptane: Security and Customizability of Software Updates for Vehicles
(2018)

A widely accepted premise is that complex software frequently contains bugs that can be remotely exploited by attackers. When this software is on an electronic control unit (ECU) in a vehicle, exploitation of these bugs can have life or death consequences. Since software for vehicles is likely to proliferate and grow more complex in time, the number of exploitable vulnerabilities will increase. As a result, manufacturers are keenly aware of the need to quickly and efficiently deploy updates so that software vulnerabilities can be remedied as soon as possible.
Automated classification of security requirements
(2016)

Requirement engineers are not able to elicit and analyze the security requirements clearly, that are essential for the development of secure and reliable software. Proper identification of security requirements present in the Software Requirement Specification (SRS) document has been a problem being faced by the developers. As a result, they are not able to deliver the software free from threats and vulnerabilities. Thus, in this paper, we intend to mine the descriptions of security requirements present in the SRS document and thereafter develop the classification models. The security-based descriptions are analyzed using text mining techniques and are then classified into four types of security requirements viz. authentication-authorization, access control, cryptography-encryption and data integrity using J48 decision tree method. Corresponding to each type of security requirement, a prediction model has been developed. The effectiveness of the prediction models is evaluated against requirement specifications collected from 15 projects which have been developed by MS students at DePaul University. The result analysis indicated that all the four models have performed very well in predicting their respective type of security requirements.
CitySpectrum: a non-negative tensor factorization approach
(2014)

People flow at a citywide level is in a mixed state with several basic patterns (e.g. commuting, working, commercial), and it is therefore difficult to extract useful information from such a mixture of patterns directly. In this paper, we proposed a novel tensor factorization approach to modeling city dynamics in a basic life pattern space (CitySpectral Space). To obtain the CitySpectrum, we utilized Non-negative Tensor Factorization (NTF) to decompose a people flow tensor into basic life pattern tensors, described by three bases i.e. the intensity variation among different regions, the time-of-day and the sample days. We apply our approach to a big mobile phone GPS log dataset (containing 1.6 million users) to model the fluctuation in people flow before and after the Great East Japan Earthquake from a CitySpectral perspective. In addition, our framework is extensible to a variety of auxiliary spatial-temporal data. We parametrize a people flow with a spatial distribution of the Points of Interest (POIs) to quantitatively analyze the relationship between human mobility and POI distribution. Based on the parametric people flow, we propose a spectral approach for a site-selection recommendation and people flow simulation in another similar area using POI distribution.
A Novel Artificial Bee Colony Algorithm Based on Modified Search Equation and Orthogonal Learning
(2013)

The artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, ABC has an insufficiency regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we first propose an improved ABC method called as CABC where a modified search equation is applied to generate a candidate solution to improve the search ability of ABC. Furthermore, we use the orthogonal experimental design (OED) to form an orthogonal learning (OL) strategy for variant ABCs to discover more useful information from the search experiences. Owing to OED's good character of sampling a small number of well representative combinations for testing, the OL strategy can construct a more promising and efficient candidate solution. In this paper, the OL strategy is applied to three versions of ABC, i.e., the standard ABC, global-best-guided ABC (GABC), and CABC, which yields OABC, OGABC, and OCABC, respectively. The experimental results on a set of 22 benchmark functions demonstrate the effectiveness and efficiency of the modified search equation and the OL strategy. The comparisons with some other ABCs and several state-of-the-art algorithms show that the proposed algorithms significantly improve the performance of ABC. Moreover, OCABC offers the highest solution quality, fastest global convergence, and strongest robustness among all the contenders on almost all the test functions.
Large-scale cluster management at Google with Borg
(2015)

Google's Borg system is a cluster manager that runs hundreds of thousands of jobs, from many thousands of different applications, across a number of clusters each with up to tens of thousands of machines.
 It achieves high utilization by combining admission control, efficient task-packing, over-commitment, and machine sharing with process-level performance isolation. It supports high-availability applications with runtime features that minimize fault-recovery time, and scheduling policies that reduce the probability of correlated failures. Borg simplifies life for its users by offering a declarative job specification language, name service integration, real-time job monitoring, and tools to analyze and simulate system behavior.
 We present a summary of the Borg system architecture and features, important design decisions, a quantitative analysis of some of its policy decisions, and a qualitative examination of lessons learned from a decade of operational experience with it.
Characterizing Machines and Workloads on a Google Cluster
(2012)

Cloud computing offers high scalability, flexibility and cost-effectiveness to meet emerging computing requirements. Understanding the characteristics of real workloads on a large production cloud cluster benefits not only cloud service providers but also researchers and daily users. This paper studies a large-scale Google cluster usage trace dataset and characterizes how the machines in the cluster are managed and the workloads submitted during a 29-day period behave. We focus on the frequency and pattern of machine maintenance events, job- and task-level workload behavior, and how the overall cluster resources are utilized.
Characterizing Cloud Applications on a Google Data Center
(2013)

In this paper, we characterize Google applications, based on a one-month Google trace with over 650k jobs running across over 12000 heterogeneous hosts from a Google data center. On one hand, we carefully compute the valuable statistics about task events and resource utilization for Google applications, based on various types of resources (such as CPU, memory) and execution types (e.g., whether they can run batch tasks or not). Resource utilization per application is observed with an extremely typical Pareto principle. On the other hand, we classify applications via a K-means clustering algorithm with optimized number of sets, based on task events and resource usage. The number of applications in the K-means clustering sets follows a Pareto-similar distribution. We believe our work is very interesting and valuable for the further investigation of Cloud environment.
The Autism Diagnostic Observation Schedule, Toddler Module: Standardized Severity Scores.
(2015)

Standardized calibrated severity scores (CSS) have been created for Autism Diagnostic Observation Schedule, 2nd edition (ADOS-2) Modules 1-4 as a metric of the relative severity of autism-specific behaviors. Total and domain CSS were created for the Toddler Module to facilitate comparison to other modules. Analyses included 388 children with ASD age 12-30 months and were replicated on 435 repeated assessments from 127 children with ASD. Compared to raw scores, associations between total and domain CSS and participant characteristics were reduced in the original sample. Verbal IQ effects on Social Affect-CSS were not reduced in the replication sample. Toddler Module CSS increases comparability of ADOS-2 scores across modules and allows studies of symptom trajectories to extend to earlier ages.